// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#ifndef EIGEN_CXX11_TENSOR_TENSOR_LAYOUT_SWAP_H
#define EIGEN_CXX11_TENSOR_TENSOR_LAYOUT_SWAP_H

namespace Eigen {

/** \class TensorLayoutSwap
 * \ingroup CXX11_Tensor_Module
 *
 * \brief Swap the layout from col-major to row-major, or row-major
 * to col-major, and invert the order of the dimensions.
 *
 * Beware: the dimensions are reversed by this operation. If you want to
 * preserve the ordering of the dimensions, you need to combine this
 * operation with a shuffle.
 *
 * \example:
 * Tensor<float, 2, ColMajor> input(2, 4);
 * Tensor<float, 2, RowMajor> output = input.swap_layout();
 * eigen_assert(output.dimension(0) == 4);
 * eigen_assert(output.dimension(1) == 2);
 *
 * array<int, 2> shuffle(1, 0);
 * output = input.swap_layout().shuffle(shuffle);
 * eigen_assert(output.dimension(0) == 2);
 * eigen_assert(output.dimension(1) == 4);
 *
 */
namespace internal {
template<typename XprType>
struct traits<TensorLayoutSwapOp<XprType>> : public traits<XprType>
{
	typedef typename XprType::Scalar Scalar;
	typedef traits<XprType> XprTraits;
	typedef typename XprTraits::StorageKind StorageKind;
	typedef typename XprTraits::Index Index;
	typedef typename XprType::Nested Nested;
	typedef typename remove_reference<Nested>::type _Nested;
	static const int NumDimensions = traits<XprType>::NumDimensions;
	static const int Layout = (traits<XprType>::Layout == ColMajor) ? RowMajor : ColMajor;
	typedef typename XprTraits::PointerType PointerType;
};

template<typename XprType>
struct eval<TensorLayoutSwapOp<XprType>, Eigen::Dense>
{
	typedef const TensorLayoutSwapOp<XprType>& type;
};

template<typename XprType>
struct nested<TensorLayoutSwapOp<XprType>, 1, typename eval<TensorLayoutSwapOp<XprType>>::type>
{
	typedef TensorLayoutSwapOp<XprType> type;
};

} // end namespace internal

template<typename XprType>
class TensorLayoutSwapOp : public TensorBase<TensorLayoutSwapOp<XprType>, WriteAccessors>
{
  public:
	typedef TensorBase<TensorLayoutSwapOp<XprType>, WriteAccessors> Base;
	typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::Scalar Scalar;
	typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
	typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
	typedef typename Eigen::internal::nested<TensorLayoutSwapOp>::type Nested;
	typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::StorageKind StorageKind;
	typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::Index Index;

	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorLayoutSwapOp(const XprType& expr)
		: m_xpr(expr)
	{
	}

	EIGEN_DEVICE_FUNC
	const typename internal::remove_all<typename XprType::Nested>::type& expression() const { return m_xpr; }

	EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorLayoutSwapOp)
  protected:
	typename XprType::Nested m_xpr;
};

// Eval as rvalue
template<typename ArgType, typename Device>
struct TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device>
{
	typedef TensorLayoutSwapOp<ArgType> XprType;
	typedef typename XprType::Index Index;
	static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
	typedef DSizes<Index, NumDims> Dimensions;

	enum
	{
		IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
		PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
		BlockAccess = false,
		PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
		Layout = (static_cast<int>(TensorEvaluator<ArgType, Device>::Layout) == static_cast<int>(ColMajor)) ? RowMajor
																											: ColMajor,
		CoordAccess = false, // to be implemented
		RawAccess = TensorEvaluator<ArgType, Device>::RawAccess
	};

	//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
	typedef internal::TensorBlockNotImplemented TensorBlock;
	//===--------------------------------------------------------------------===//

	EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
		: m_impl(op.expression(), device)
	{
		for (int i = 0; i < NumDims; ++i) {
			m_dimensions[i] = m_impl.dimensions()[NumDims - 1 - i];
		}
	}

#ifdef EIGEN_USE_SYCL
	// binding placeholder accessors to a command group handler for SYCL
	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler& cgh) const { m_impl.bind(cgh); }
#endif

	typedef typename XprType::Scalar Scalar;
	typedef typename XprType::CoeffReturnType CoeffReturnType;
	typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
	typedef StorageMemory<CoeffReturnType, Device> Storage;
	typedef typename Storage::Type EvaluatorPointerType;

	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }

	EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data)
	{
		return m_impl.evalSubExprsIfNeeded(data);
	}
	EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }

	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { return m_impl.coeff(index); }

	template<int LoadMode>
	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
	{
		return m_impl.template packet<LoadMode>(index);
	}

	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
	{
		return m_impl.costPerCoeff(vectorized);
	}

	EIGEN_DEVICE_FUNC typename Storage::Type data() const { return constCast(m_impl.data()); }

	const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }

  protected:
	TensorEvaluator<ArgType, Device> m_impl;
	Dimensions m_dimensions;
};

// Eval as lvalue
template<typename ArgType, typename Device>
struct TensorEvaluator<TensorLayoutSwapOp<ArgType>, Device>
	: public TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device>
{
	typedef TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device> Base;
	typedef TensorLayoutSwapOp<ArgType> XprType;

	enum
	{
		IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
		PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
		BlockAccess = false,
		PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
		Layout = (static_cast<int>(TensorEvaluator<ArgType, Device>::Layout) == static_cast<int>(ColMajor)) ? RowMajor
																											: ColMajor,
		CoordAccess = false // to be implemented
	};

	//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
	typedef internal::TensorBlockNotImplemented TensorBlock;
	//===--------------------------------------------------------------------===//

	EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
		: Base(op, device)
	{
	}

	typedef typename XprType::Index Index;
	typedef typename XprType::Scalar Scalar;
	typedef typename XprType::CoeffReturnType CoeffReturnType;
	typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;

	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
	{
		return this->m_impl.coeffRef(index);
	}
	template<int StoreMode>
	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index index, const PacketReturnType& x)
	{
		this->m_impl.template writePacket<StoreMode>(index, x);
	}
};

} // end namespace Eigen

#endif // EIGEN_CXX11_TENSOR_TENSOR_LAYOUT_SWAP_H
